PERSONNEL SELECTION BASED ON THE SELF-CONFIDENCE LEVEL OF THE DECISION MAKER:

A FUZZY APPROACH

GUNAY IMANOVA

MASTER’S THESIS

NICOSIA 2019

NEAR EAST UNIVERSITY GRADUATE SCHOOL OF SOCIAL SCIENCES

BUSINESS ADMINISTRATION PROGRAM

PERSONNEL SELECTION BASED ON THE SELF-CONFIDENCE LEVEL OF THE DECISION MAKER:

A FUZZY APPROACH

GUNAY IMANOVA

NEAR EAST UNIVERSITY GRADUATE SCHOOL OF SOCIAL SCIENCES BUSINESS ADMINISTRATION PROGRAM

MASTER’S THESIS

THESIS SUPERVISOR PROF. DR. SERIFE Z. EYUPOGLU

NICOSIA 2019

We as the jury members certify the ‘Personnel Selection Based on the Self-Confidence Level of the Decision Maker: A Fuzzy Approach’

prepared by GUNAY IMANOVA defended on 14/01/19 Has been found satisfactory for the award of degree of Master

ACCEPTANCE/APPROVAL

JURY MEMBERS

......................................................... Prof. Dr. Şerife Zihni Eyüpoğlu (Supervisor)

Near East University Faculty of Economics and Administrative Sciences/Department of Business Administration

......................................................... Prof. Dr. Tülen Saner (Head of Jury)

Near East University School of Tourism and Hotel Management

......................................................... Prof. Dr. Rahib H. Abiyev

Near East University Faculty of Engineering/Department of Computer Engineering

......................................................... Prof. Dr. Mustafa Sağsan

Graduate School of Social Sciences Director

DECLARATION

I am Gunay Imanova, hereby declare that this dissertation entitled ‘Personnel Selection Based on the Self-Confidence Level of the Decision Maker: A Fuzzy Approach’ has been prepared myself under the guidance and supervision of Prof. Dr. Serife Zihni Eyupoglu in

partial fulfillment of The Near East University, Graduate School of Social Sciences regulations and does not to the best of my knowledge breach any Law of Copyrights and has

been tested for plagiarism and a copy of the result can be found in the Thesis. o The full extent of my Thesis can be accessible from anywhere.

o My Thesis can only be accessible from Near East University. o My Thesis cannot be accessible for two (2) years. If I do not apply for

extension at the end of this period, the full extent of my Thesis will be

accessible from anywhere.

January 14, 2019

Gunay Imanova

iii

ACKNOWLEDGEMENTS

I am using this opportunity to express my gratitude to my thesis supervisor

Prof. Dr. Serife Zihni Eyupoglu, whose office door was always open

whenever I am in trouble for my research. I would like to thank for her

truthful, kind and relaxing guidance and advices during intensive period of my

writing.

I would like to express my warm thanks and gratitude to my external expert

guide Prof. Dr. Rafik Aliev for his guidance and support with his profound

knowledge. It was a privilege to work and learn under their precious and

constructive guidance.

I would also like to thank my instructors, who have contributed to enhance

my knowledge and above all support me to be ambitious in learning.

Especially I would like to mention my sincere thanks to Prof. Dr. Tulen Saner.

I express my warm and sincere thanks to my friends who have always

motivated me towards achievement and always rejoiced my heart with their

endless support and pure love.

Finally, I am grateful to my sister and my parents, who have encouraged,

guided, loved, supported, and helped me through my entire life without

getting tired. Besides, I would like to indicate my sincere and genuine thanks

to other dear family members.

Once and for all, I would like to dedicate all my endeavor and thesis

research to my two dear angel grandmothers, Mrs. Gulshad and Mrs. Aida, I

hope you feel...

iv

ABSTRACT

PERSONNEL SELECTION BASED ON THE SELF-

CONFIDENCE LEVEL OF THE DECISION MAKER: A FUZZY

APPROACH

Personnel selection is considered as the decisive factor of the companies’

overall success rate. Appropriate personnel selection is desired in order to

enhance the human capital of an organization. As the nature of all selections,

personnel selection involves the decision making process. In other words, for

effective and efficient personnel selection process managers (or accountable

subordinate) who are decision makers (DM), should generate and analyze

the possible alternative candidate pool for a vacant position. In order to select

the most appropriate and suitable candidate it might be needed to use

several effective tools such as linear programming or LP model to be able to

obtain the overall ranking weights for the candidates and to minimize the sum

of information deviation between the judgment or preference relations of DM

and the priority vector w. A novel approach for more robust decision making

and solution is based on fuzzy preference relations with indicated self-

confidence levels of the decision makers.

Although existing literature involves various types of preference relations

such as linguistic preference relations, fuzzy preference relations,

multiplicative preference relations, and so forth, in these formats self-

confidence level of DM is not taken into account. In this paper personnel

selection decision making process is investigated to select the most suitable

or optimal candidate for a vacant faculty position by considering linguistic

self-confidence level of DM based on preceding provided preference value.

Validity of the new considered approach has been proven by the numerical

examples that comprise 5 alternatives and 5 criteria.

Keywords: Decision making, Personnel selection, Fuzzy preference

relations, Self-confidence levels, Linguistic preference relations, Linear

programming, Human resources management.

v

ÖZ

KARAR VERİCİNİN ÖZGÜVEN DERECELERİNE BAĞLI

PERSONEL SEÇİMİ: BULANIK MANTIK YAKLAŞIMI

Personel seçimi, şirketlerin genel başarı oranlarının belirleyici unsuru olarak

görülmektedir. Organizasyonların beşeri sermayelerini geliştirmeleri için

doğru personel seçimi yapmaları gerekmektedir. Bütün seçim süreçlerinde

olduğu gibi personel seçiminde de karar verme eylemi gerekmektedir. Diğer

bir deyişle, etkili ve verimli personel seçimi için karar verici durumunda olan

İnsan Kaynakları yöneticisi (veya yetkili alt personel) açık pozisyona esasen

yeterli sayıda uygun aday havuzu oluşturmalı ve uygun adayları

incelemelidir. Karar verici doğru ve en uygun aday seçiminde Doğrusal

Programlama (DP) gibi bazı etkili araçlardan yararlanarak adayların öncelik

derecelerini ve sıralamasını öğrenebilir ve karar vericinin alternatifler

üzerindeki tercih ilişkileri ile öncelik vektörü arasındaki bilgi sapmasını da en

aza indirgenebilinir. Oluşturulan yeni ve daha sağlam karar verme yaklaşımı

bulanık tercih ilişkisi ve özgüven derecelerini ele almaktadır.

Daha önceki edebiyat çalışmalarında bulanık tercih ilişkisi, dilsel tercih ilişkisi

ve çarpımsal tercih ilişkisi gibi birçok tercih ilişkileri konu edilmiş, fakat bu

tercih ilişkilerine ilişkin özgüven dereceleri veya seviyeleri konu alınmamıştır.

Bu çalışmada, karar vericilerin bulanık tercih ilişkileri üzerine belirttikleri dilsel

özgüven dereceleri dikkate alınarak personel seçimi için karar verme

sürecine ilişkin araştırmalar ve karşılaştırmalar yapılmış ve en uygun adayın

seçilmesi sağlanmıştır. Yeni yaklaşımın geçerliliği örnek problemlerle

kanıtlanmıştır.

Anahtar Kelimeler: Karar verme, Personel seçimi, Bulanık tercih ilişkileri,

Özgüven dereceleri, Dilsel tercih ilişkileri, Doğrusal programlama, İnsan

Kaynakları yönetimi.

vi

TABLE OF CONTENTS

Pages

ACCEPTANCE/ APPROVAL i

DECLARATION ii

ACKNOWLEDGEMENTS iii

ABSTRACT iv

ÖZ v

CONTENTS vi

LIST OF TABLES viii

LIST OF FIGURES ix

ABBREVATIONS x

INTRODUCTION 1

CHAPTER 1

DECISION MAKING PROCESS 2

1.1 Identifying Problem/Problem Recognition 3

1.2 Generating/Developing Alternatives 4

1.3 Evaluating/Analyzing Alternatives 5

1.3.1 Decision Making Criteria 10

1.4 Making a Choice 11

1.4.1 Making Better Decisions 11

1.4.2 Different Decision Making Styles 13

1.4.3 Psychological Traps That Cause Biases in Decision Making 19

1.5 Conclusion for Chapter 1 20

CHAPTER 2

HUMAN RESOURCE MANAGEMENT (HRM) AND

PERSONNEL SELECTION PROCESS

2.1 Importance of the HRM 21

2.2 The Recruitment and Selection Processes 22

vii

2.2.1 Recruitment 22

2.2.2 Selection 24

2.2.2.1 Interviews for Selection 25

2.2.2.2 Personality/Psychometric Tests 27

2.2.2.3 Clinical and Statistical Approaches 29

2.3 Conclusion for Chapter 2 29

CHAPTER 3

RESEARCH METHODOLOGY

3.1 Preference Relations-Representation of the Preferences 31

3.2 Fuzzy Preference with Self-confidence 33

3.3 Preliminaries 33

3.4 Methodology for the Solution 37

CHAPTER 4

RESULTS 43

DISCUSSION AND CONCLUSION 49

REFERENCES 51

APPENDIX 60

PLAGIARISM REPORT 61

viii

LIST OF TABLES Pages

Table 1: Fuzzy Preference Relation with Respect to C1 39

Table 2: Aggregated Preference Matrix 40

Table 3: Random Index (RI) 40

Table 4: Eigenvector Calculation 41

Table 5: λmax (Maximum Eigenvalue) Calculation 41

Table 6: Fuzzy Preference Relation with Self-Confidence 43

Table 7: Alternative Example 46

ix

LIST OF FIGURES Pages

Figure 1: The Decision Making Process 3

Figure 2: Hierarchical Structure for a Linguistic Variable and Values with

Associated Compatibility 8

Figure 3: Decision Style Model 18

Figure 4: Compatibility Function Example for ‘young’ Labeled Fuzzy

Restriction 37

Figure 5: Research Model 39

Figure 6: Triangular Fuzzy Numbers 44

x

ABBREVATIONS

AHP Analytic Hierarchy Process

CI Consistency Index

CR Consistency Ratio

DM Decision Maker

HR(M) Human Resources (Management)

LP Linear Programming

MCDM Multi-criteria Decision Making

NF Intuition-Feeling

NL Natural Language

NT Intuition-Thinking

RI Random Index

SF Sensation-Feeling

ST Sensation-Thinking

TOPSIS Technique for Order Preference by

Similarity to Ideal Situation

1

INTRODUCTION

In our continuing lives we have to make various critical non-programmed and

programmed decisions. Individuals need to choose; the movie to watch; the

meal to cook; the university and field to study; the job to work; the strategy to

follow for organizational success; and so forth. Decision making is simply the

process of generating and analyzing feasible alternatives and selecting one

or more of them as required (Dessler, 2004). Judgment, rules, heuristics,

intuition, and creativity are all the key factors of decision making process.

Personnel selection is one of the important decisions to be made by the

manager; Human Resources manager in bigger companies or by general

managers or other related decision makers (DMs) in smaller companies in

order to find the most appropriate or optimal candidate to match with the job

requirements, and in turn to achieve the organizational goals and objectives.

The organizational goal may be an increase in revenue by 30% (percent) in

four years, or rise in profit or productivity by 5.5% in two years. To achieve

this goal manager may have to motivate and influence the personnel towards

achieving more by offering incentives (financial, praise, recognition, benefit,

and so forth), and may have to hire more associated personnel as required.

Hiring optimal personnel is crucial for the overall success of the organization.

Research Objective: Our purpose is to identify the best candidate for a

particular vacant faculty position taking into account the novel preference

representation format with self-confidence levels of the DMs. Fuzzy logic is

used in order to cope with the uncertainty and incomplete information

presented in expressing or indicating the preference of one alternative

candidate over another alternative candidate with respect to criteria. Using a

new approach we propose the use of self-confidence levels on indicated

fuzzy preference evaluations (according to judgment based on interview and

test results) of mangers or other related DMs who deal with the personnel

selection problem to decrease the information loss. By deriving a linear

programming model we intend to minimize the information deviation between

the DM’s preferences and the priority vectorw .

2

CHAPTER 1

THE DECISION MAKING PROCESS

As a nature of a human being, we are perpetually subject to decision making

even in our daily simple problems. Deciding on what to eat, which university

to apply and study, the most suitable job to work on, how to improve or

develop business in a new direction through global expanding, whether to

shrink or pursue the standing position and wait for the best time to take an

action in the market, and all other such kind of related issues need decision

making capability (Aliev, Huseynov, 2014). According to Adair (2000)

decision making process comprises some stages such as problem

recognition, collection of data or information, developing alternatives to be

evaluated, choosing an alternative, and finally implementing decision and

evaluating the final results for constant and continued efficacy and

effectiveness.

Dessler (2004) and Solomon (2007) describe problems as the difference

between desired and the actual state, and it stimulates most decisions.

Decision is stated as a choice among available alternatives. Decision making

is simply the process of generating and analyzing possible available

alternatives and selecting one or more of them Dessler (2004).

Lunenburg (2010), states that decision making is a recyclable and an

iterative process that follows a logical sequence. For instance, to generate

and choose an alternative, decision maker (DM) must identify the problem

first and so forth. Recyclable means if any problem exists in any stage DM

can turn back to fix the problem aroused. It is shown in the figure 1.

3

Figure 1: The Decision Making Process

(Lunenburg, 2010)

1.1 Identifying Problem/Problem Recognition

Solomon (2007) conceived that problem recognition occurs whenever a

person perceives that there is a small or big, simple or complicated problem

that is needed to be solved. He explains it with an explicit example; a driver

has a problem if he/she is at the end of his/her petrol in an unanticipated time

in a highway. The problem can also be recognized if there is a dynamic

environment in which “standard of comparison” or the working environment

for an individual is changed. For instance, a manager can be used to be an

autocratic leader who is the only one to make related organizational

decisions without regarding the employees’ ideas. However, as the

expectations or the standard of comparison of both the employees and the

organizations are changing and developing, manager alters her/his

leadership style to other styles such as democratic or participative style in

which the leader (manager) highly considers the decisions and the ideas of

the employees while making organizational decisions. On the other hand,

manager can change her/his leadership style when she/he recognizes

something going wrong with the democratic leadership style, and can use

backup style or in other words adopt autocratic leadership style to take more

control over employees and making related decisions.

4

Human resource management department has organizational goals and

objectives to accomplish for instance; effective and valid recruitment process

(that includes personnel selection, training and development etc.) which in

turn will reduce employee training and employee turnover costs, increase

productivity and performance, and commitment (Harlan & Anne, 1980).

Thereby, setting these goals and objectives lays the groundwork for

identifying related problems, deciding which actions to follow, and finally

evaluating the results (Lunenburg, 2010). Consequently, decision is

considered as an effective decision if it was able to solve the identified

problem, in other words if manager was able to accomplish the desired goals

and objectives. Lunenburg (2010) states that effective decision maker

realizes the significance of correctly identifying the related problems.

According to Kepner and Tregoe (1997), first stage of decision making (i.e.

identifying problem) is the critical phase to reach a sound decision as a result

and also it is easier to decide on what the problem is not. They noted that

better problem definition leads to better quality decisions at the end.

Problems can be identified by internal and external environment auditing

(Lunenburg, 2010). Problem is realized according to the obstacles that

provide dissatisfaction. So, managers have to constantly control the progress

of their organizational activities in order to follow a smooth path which directs

to achievement of desired goals and objectives. To uncover possible threats

and opportunities that might affect goals and objectives managers can do risk

analysis with tools such as Scenario and SWOT analysis (Hillson, 2002).

These analyses can be used whenever decisions such as whether to expand

the business are considered.

Lunenburg (2010) also brought forward that identifying problem must be

explained with its situation that specifically causes the problem. Hence, we

can achieve the desired solution or decision as a result.

1.2 Generating/Developing Alternatives

Casey, Getz, and Galvan (2008) state that under intensive negative feelings

such as panic, anger, anxiety, and doubt people usually avoid generating

5

alternatives and they are focused on deciding at once without searching for

alternative.

According to Stevenson (2009), the ability to make successful decisions

usually associated with generating suitable alternatives or criteria. The

decision makers have to develop and identify such alternative that can be

used as a solution of the problem. He states that identifying alternatives or

criteria depends on cost-profit analysis, improved productivity, return on

investment, image of the company, increase in demand and other related

subjects. For instance, investing in advanced personnel selection strategies

will return back with low turnover rate and accordingly low costs. The

creativity and the experience that a decision maker possesses under the

given situation nature, usually determine the possible quality and number of

criteria or alternatives. Identifying alternatives always carries risk of

overlooking (i.e. leaving out of account) other more possible predominant

alternatives. As a result, the solution reached might not be the optimal as it is

thought. On the other hand it might be not possible to identify or develop all

the possible alternatives because of the need for more deal of time, money

and creativity. However, it is a well-known principle that through more sound

exertions on developing alternatives, more justified and optimal decisions can

be made accordingly (Stevenson, 2009). Dessler (2004), states that expert

decision makers see alternatives as “the raw materials or elements of

decision making". According to them alternatives show a particular set of

potential choices that a manager needs to achieve for desired objectives. To

develop good alternatives decision makers must discover their creativity. This

can be realized by trying to develop as many alternatives as possible. Next

step is to expand searching for more information benefiting from experts’

experience, and other related people. The main point here is to remember

the targeted objectives, and by asking “How to achieve?” question decision

maker is able to generate related good alternatives (Dessler, 2004).

1.3 Evaluating/Analyzing Alternatives

Analyzing and comparing alternatives is the next step in decision making

process. Evaluation process is usually enhanced by using different statistical

6

or mathematical tools and techniques such as linear programming

(Stevenson, 2009).

According to Dessler (2004), decision making carries risk and danger as it

mentioned earlier. He explains it as making decision today and feeling it

tomorrow. For instance, HR manager hires the personnel today with a higher

salary then other companies offer to get a competitive advantage, however,

after one month manager notices that paying such a higher amount of salary

makes other personnel reluctant because of unjust treatment. Here another

question arises; “Given the right objectives how to select an appropriate

alternative?”. Hereby, it is considered as the most difficult and complex part

of decision making process which needs future forecasting. Perfect or certain

conditions and environments comprise the relevant parameters such as

stable demand for the company’s products or services, and the cost of

production, which have known consequences and values for the future. Since

the outcomes are known, decision makers make their decisions under

certainty. According to Statistical Decision Theory, in organizational problem

solving and decision making processes managers confront with three

conditions of environments; which are certainty, risk, and uncertainty.

Certainty condition under classical theory can be reached with a relatively

stable economy and competitive equilibrium. Simon (1979) describes the

decision making under uncertainty as bounded rationality that is used to

replace the classical theory of rational choice of human beings. This new

model describes how decisions should be made when DM has to search for

alternatives that have imperfectly known consequences (such as expected

values of future sales, and demand) because of uncertainty exists in the

environment and the limited computational capabilities. Simon (1979) states

that utility maximization is not always desired in searching for alternatives.

Instead, DM terminates the searching for other possible alternative whenever

he/she finds the convenient one that meets his/her aspiration and desire for a

good alternative. He called this selection model “satisficing”. In decision

making approach DM can be satisfied by either discovering the optimal or

best solution by working out the burden of mathematical computations to

7

tackle a more simplified world problem or choosing the satisfactory solution

that is more relevant to a realistic world situation. Simon (1979) also states

that both approaches are used interchangeably in management science

problems. With this new approach, Simon (1979) brought forward that human

beings cannot be always fully rational. Therefore, Zadeh (1975) has

suggested an approach that can manage the uncertainties in real decision

making processes. The theory suggested by Zadeh (1975) is fuzzy set

theory, which can be used as the mathematical fundamentals for the creation

of an eligible formal basis for bounded rationality ideas for more realistic

decisions (Aliev & Huseynov, 2013). Aliev and Huseynov (2013) state this

relationship on the basis of imperfect or limited information knowledge of

human beings (which is the basic focal point of bounded rationality), and their

desire for evaluating information in linguistic variables. Imperfect information

refers to vague, uncertain, unreliable, indefinite/incomplete, imprecise or

partly true information (Zadeh, 2009). Zadeh (2009) as cited in Imanov,

Ozkilic, and Imanova (2017) states that it is very rare for the information

presented in a real world situation or problem to be the perfect information.

Fuzzy set theory includes linguistically described imperfect information.

According to Zadeh (1975), linguistic variable has values in the form of words

or sentences in an artificial or natural language (NL). This results from the

limited computational abilities of human beings, who are thinking and

reasoning in natural language propositions instead of complex mathematical

expressions. From another point of view, human beings who have limited and

imprecise knowledge prefer linguistic evaluations that allow vagueness,

uncertainty, and impreciseness in decisions. Consequently, DMs achieve

satisfactory and approximate results (as bounded rationality suggests) and it

is called approximate reasoning in fuzzy logic (Aliev & Huseynov, 2013).

Approximate reasoning connotes neither exact nor very inexact reasoning

(Zadeh, 1975). To put in another way, approximate reasoning is the process

of achieving a possible imprecise result from the given imprecise

assumptions (Pal & Mandal, 1991). Therefore, fuzzy logic is dissimilar with

the classical Boolean logic.

8

Zadeh (1975) explains linguistic variable with some examples; for instance,

age, temperature, height are linguistic variables if the given values are

linguistic (qualitative) instead of numerical. Age has values such as young,

not young, very young, old, not very young, not very old, etc., instead of 21,

22, 23, 24, etc. So linguistic variable’s (e.g. Age) values (young, old) are

generated from the primary terms (age), a collection of hedges such as very,

slightly, extremely, quietly, etc., and connectives of ‘and’ and ‘or’. However,

age is generally a numerical variable so; we use compatibility function that

associates with the linguistic value of the given variable. For example, value

or fuzzy subset ‘old’ A associates each age within the given age interval (e.g.

[0, 100]) with a real number in the interval [0, 1], that shows the compatibility

or grade of membership of any age u in the linguistic value ‘old’ A, μA(u), μA:

U → [0, 1]. Compatibility of age 80 with ‘old’ might be 0.8; on the other hand,

27 might be 0.2.

Figure 2: Hierarchical Structure for a Linguistic Variable and Values with Associated

Compatibility

(Zadeh, 1975)

Furthermore, Pal and Mandal (1991) as cited in Zadeh (1975) state that

Boolean’s two valued logic of truth values or propositions that can be either 0

or 1 (false or true, respectively), is limited with crisp binary values and

intolerable to imprecise and incomplete information. However, fuzzy logic

also provides the intermediate values within the crisp values of 0 and 1. Truth

9

qualifications such as very true, not very true, false; possibility qualifications

such as possible, almost possible, almost impossible; and probability

qualifications such as likely, unlikely, more likely, extremely likely, probable,

improbable, are all three basic qualification types of fuzzy logic that enable

the linguistic responds with human reasoning to the questions such as “Is

North Cyprus close to Turkey?”. The answer may be fairly true, quite true, not

very true, and so forth.

As it is stated in Aliyeva (2017) classical decision making approaches are not

practical in uncertain information or vagueness existing environment. In such

an environment DM is provided with extended classical multi-criteria decision

making (MCDM) in other words, fuzzy (type-1 and type-2) MCDM that enable

DM to explain his/her preferences in incomplete information and to reach a

sound decision.

Dessler (2004) states three steps for an effective evaluation of alternatives.

Firstly, through process analysis DMs have to put themselves into the future

in their minds. As Dessler (2004) states anticipating and looking into the

future or tomorrow is a valuable analytical skill. In his book he states process

analysis as solving the existing problem by thinking broadly from the

beginning to the end of the process, using the imagination at each phase to

guess what actually could happen by choosing an alternative.

Secondly, it is suggested to eliminate inferior alternatives or in other words,

delete them from the possible alternatives list if they have little or no

possibility of success.

And finally, author proposes organizing the rest of alternatives in

Consequences matrix form. This consequences matrix or table shows the

DM’s objectives (horizontally), and alternatives (vertically). In each box of the

table, there is a short statement that shows the related consequences of one

alternative to the associated objective.

10

1.3.1 Decision Making Criteria

As Stevenson (2009) states, DMs develop a payoff table that estimates, or

determines the payoff or outcome related to each possible future conditions

or state of nature. Evaluation process of alternatives is conducted through

decision criteria for uncertainty such as;

• Maximin is choosing the best possible payoff alternative among the

worst possible payoffs. This criterion is based on pessimistic

approach.

• Maximax is choosing the alternative with best payoff or outcome,

without considering any payoff lower than the best. This is an

optimistic approach.

• Laplace criterion relies on suggestion that the states of nature are

equally possible. With this approach DM chooses the alternative with

the best average outcome or payoff among all other alternatives.

• Minimax regret criterion seeks to minimize the regret by minimizing the

separation between the best possible payoff alternative (for each state

of nature) and the given possible payoff.

Decision making under risk lies between the certainty and uncertainty

dominated environments. Here, for each state of nature the probability of

occurrence is known. Generally, expected monetary value criterion approach

is used to make decisions under risk. The expected value is computed by

summing up all the weighted payoffs (multiplying the related probability of

occurrence by the payoff for each state of nature) for an alternative given.

The alternative with the highest expected value is selected.

A decision tree is another approach to be used in decision making instead of

payoff tables, especially for evaluating situations which involve sequential

decisions such as whether to expand a company after realizing a higher

demand for goods/services than expected. Its branches and nodes, i.e.

schematic representation shows the possible available alternatives and their

possible outcomes, payoffs, or returns. As a result of analyzing, alternative

with the highest expected value or return is chosen as a decision.

11

DM may want to obtain the perfect information for example about consumer

behavior for marketing research. To receive that additional information DM

might have to pay money to related legal experts. Expected value of perfect

information is the equation (expected payoff under certainty - expected payoff

under risk) to find the maximum amount of tolerable cost or spending to

obtain this additional information (Stevenson, 2009).

When DM confronts with the two states of nature such as low demand and

high demand, it might be useful to conduct sensitivity analysis in order to

designate the range of probability that gives the same alternative with the

best payoff, in other words, graph shows how much the probability for state

of nature can be changed to still obtain the same best alternative.

1.4 Making a Choice

After evaluating and eliminating the alternatives in the previous stage DM has

left with two or more possible alternatives (Lunenburg, 2010). DM should

make a choice according to main original objective such as attaining newly

graduated personnel for a vacant position in finance department. However if

the cost of training was important, manager would think about other

convenient candidates who does not need the program of training and

development which indicates more spending. Dessler (2004) declared that

unless the right choice is made analyses conducted by a DM is useless.

Gilboa (2010) proposes to choose the alternative that is feasible, satisfactory,

and acceptable. Lunenburg (2010), states that the DM might be able to

choose more than one alternative simultaneously. Author explains it with a

simple example of hiring an English teacher. If the principal is between two

strong candidates, he/she can propose the vacation to one candidate and

keep the other candidate under observation. So, if the first candidate does

not meet the standards, school principal will already have the good

alternative to replace the teacher.

1.4.1 Making Better Decisions

Dessler (2004) put forward some techniques that can help the DM to improve

the quality of making decisions.

12

1.4.1.1 Increasing Knowledge

As a first step, increasing the knowledge is advised. Even for simple and

daily decisions basic information is needed. For more complicated decisions

more knowledge and information is required. To increase the knowledge DM

may always ask more and more questions such as “Who? What? When?

Where? Why? How much?”. For example who is selling the land and why?,

how much can you afford to pay? etc. are all objective related questions.

Experience is the treasure for decision making that cannot be replaced.

Internship during education, entrepreneurship, global expansion, marketing

development, etc. are all act as negative and positive directors for upcoming

operations.

Using consultants’ or other people’s experience which the DM lacks is a

substantive issue.

Carrying out research relative to the targeted objective can enable the DM to

achieve more information. For example, searching for the trade barriers,

culture, and traditions adopted in the country that the company wants to enter

will give a rich information base about its economic, politic, sociologic, etc.

situations.

1.4.1.2 Intuition of DM

Dessler (2004) explains intuition as a cognitive process. An individual makes

decisions instinctively, based on that individual’s accrued knowledge and

experience. Intuitive decision makers rely on trial-and-error method, because

without considering much more available information, they tend to try one

alternative after another and so on until the DM finds the best or optimal

alternative according to their inner nature. On the other hand, systematic

decision makers tend to follow a more logical and step by step approach in

order to solve a problem. Studies about both types of decision makers

indicates that, DMs in situations which allows enough time for the evaluation

13

of all possible alternatives, can be systematic. However, using instincts for

better decisions is also needed.

1.4.2 Different Decision Making Styles

Since many years, researchers have been studying on different decision

making styles adopted by DMs. For example, school principals, HR

managers, sales directors, finance managers, etc. are all responsible for

management functions of planning, organizing, leading and controlling, and

all of these management functions (regardless of title or level of managers)

involve the decision making. (Dessler, 2004). However, managers and other

DMs make decisions using different approaches or styles. Therefore, it is

worthwhile to discuss different decision making styles.

General Decision-Making Style (GDMS)

Scott and Bruce (1995) have suggested that decision making style is closely

associated with the individual’s customary and learned response, rather than

his/her personality traits, in a given specific situation. General Decision-

Making Style involves five decision maker categories; intuitive, rational,

spontaneous, avoidant, and dependent.

• Intuitive: This style relies on an individual’s inner senses or feelings of

what is right rather than searching for a rational source of reason in

making decision. Scott and Bruce (1995) stated that intuitive decision

makers prefer to use their “hunches and feelings” primarily in decision

making.

• Rational: Decision makers like to search in detail to gather correct

information, and evaluate the alternatives in a reasonable manner.

Primary purpose of the rational style is to make logical and systematic

decisions, by considering all angles of the alternatives chosen to

achieve the required goals and objectives. Planning careful decisions

(programmed decisions), and verifying the source of information are

other characteristics of rational decision making style.

14

• Spontaneous: Decision makers generally have impulsive nature during

making decisions. They make quick decisions that are seemed as

natural or native. Time is important so decision maker act immediately

to complete the decision making phase, as soon as possible.

• Avoidant: Decision-maker has propensity to delay or cancel the

decision making process as much as possible, and make last minute

appeal decisions whenever they confronted with pressure. He/she

probably has unsettling feelings towards making decisions and that

can be the reason to avoid decision making.

• Dependent: Dependent decision maker usually refer to or ask

somebody for advice before making important decisions. Support for a

direction from others is critically important for a decision maker.

Myer’s (1976) work on “human information process” is one of the preliminary

approaches towards decision making styles, and this study would provide a

basic for further implications and studies. Carl G. Jung’s work on

“psychological types” explains the variances in human behavior which are

associated with the individuals’ dissimilar preferences to use their minds as a

result of difference in mental functioning or use of different hemispheres of a

brain. Individuals’ discrete perception and judgment of situations are all

related concepts for differences in behavior. His theory of psychological types

helped Jung to formulate a model for methodological investigation of these

differences in human behavior. Psychological types consist of four

dimensions which are Extraversion – Introversion (EI), Sensation– INtuition

(SN), Thinking-Feeling (TF), and Judging-Perceiving (JP). Although these

four dimensions or combinations provide a matrix of 16 distinct personality

types, four main styles among them can be taken to produce decision making

styles by taking into account that while perception is carried out by Sensation

S, and/or Intuition N, judgment is based on Thinking T, and/or Feeling F.

Hence four decision making styles are produced by congregating above

mentioned two ways of Perceiving with two ways of Judging. The four styles

produced are sensation-thinking (ST), sensation-feeling (SF), intuition-

15

thinking (NT), and intuition-feeling (NF). The ST decision making style owned

individual or manager gives full weight to the facts and details that can be

gathered and verified rationally by her/his senses. This style is mainly based

on strict rules. The SF decision making style owned individual, also

emphasize perception by her/his sensations. However, in this case individual

likes to judge the situations by her/his feelings or creating a friendly and a

sympathetic environment. NT style users prefer to seek for new truths rather

than what is seem to be true by only taking the summary of the situations.

They rely upon to see the big picture rather than a part of a puzzle. NF style

user individuals bear combinations of intuitive and feeling characteristics for

perception and judgment respectively. NF processing style relies upon the

ability of understanding and communicating with the people in an enthusiastic

and insightful manner (Myers, 1976). They can be illustrated by the according

vocations as; ST as a technician, NT as a planner, SF as a teacher, and NF

as an artist. Furthermore, description of the above mentioned decision

making styles depend more on how the individual judge (process) the

information, rather than how it is perceived or gathered (Taggart & Robert,

1981).

Individuals as managers should be flexible in terms of processing decision

making styles (Barnard & Chester, 1938), to be able to survive in or cope

with the changing business management environment. Due to these changes

managers should be able to fit their decision making styles to different

confronted situations. Hence, mangers may sometimes need to decide as a

technician, planner, teacher, or an artist (Taggart, Robert, 1981).

According to Taggart and Robert (1981) as cited in Ogarca (2015), managers

could be distinguished related to their approached decision styles such as;

“Improve your work performance or you will be dismissed from your post!”

(Factual, impersonal, practical) can be an example response of a ST

manager, “If your performance does not improve, you will be sent to another

post” (Prospective, impersonal, logical) might be a NT manager’s approach

which is a more middle-of-the-road course of conduct, manager might

embrace the SF style and say “You need to evolve yourself, what can we do

to help you develop yourself?” (Factual, personal, sympathetic and friendly),

16

and lastly, “You are able to increase your work performance, we want to offer

a suggestion” (prospective, personal, enthusiastic) can be an example

approach of a NF style adopting manager. As a result, managers should bear

in mind that they can embrace any one of the above mentioned decision

making styles that fits best to the different situational factors such as

restricted time frame, behavior of the subordinate employee, or team

principles.

One of the best known decision making style models is stated by Rowe and

Mason (1987). According to this model, individuals use dominantly either

their right hemisphere or left hemisphere of brain. Rowe and Mason (1987)

stated that style of an individual depends on his/her specific tendency to think

and act in a given situation. In other words, decision style of a person is the

way, that he/she perceives and understands the stimulus and responds it in a

preferred manner, according to personality and background. They identified

two key dimensions that describe the working of our mind. They are cognitive

complexity and value orientation. An individual may have a high tolerance to

uncertainty or low tolerance to uncertainty. Values can be directed towards

human and social concerns or towards technical concerns and duties.

Thereby, composition of these two dimensions can generate four Cognitive

Complexity Model decision making styles: directive, analytical, conceptual,

and behavioral.

• Directive style is characterized by low cognitive complexity for which

individuals use less complex mental structures to find the nuances in a

given situation, and associated with the Jung’s sensing-thinking or ST

style (Robert James Moretti, 1994). They have low tolerance for

ambiguity and are fact oriented. They value tasks and technical

concerns. Directive managers involve in structured (or planned) tasks,

and technical concerns. They are punctual and offer quick satisfying

but not necessarily optimal solutions among limited alternative

solutions. Managers bearing directive style tend to be more autocratic

or dominative over other employees.

17

• Analytical style is related to Jung’s preference of intuition-thinking or

NT style. Analytical thinkers generally have high tolerance for

ambiguity, and directed towards task and technical concerns. They try

to reach the possible extensive perspective through analysis or

examination, planning, and forecasting. Although both directive and

analytical styles are autocratic and logical in approach, in analytical

approach, individual evaluates the alternatives to make the optimal

decision.

• Conceptual style is associated with Jung’s typology of intuition-

feeling or NF style and has high tolerance for ambiguity and

uncertainty. Conceptual thinkers are oriented to social concerns and

highly value people. They are idealist thinkers, and like leaders

encourage participation and contribution. They focus on ethics in

conducting business. Conceptual style is characterized by the need for

achievement through exploring new opportunities, formulating new

strategies, and being creative and risk taker, in a large time period.

They need recognition, freedom, praise or positive feedback.

• Finally, behavioral style related to Jung’s perspectives of sensing-

feeling or SF is characterized by low tolerance for ambiguity (low

cognitive complexity). Behavioral style decision makers value people

and social issues. They prefer face-to-face communications and

discussions rather than written statistical or quantified reports as in the

case of conceptual style. They consider the subordinates’ decisions

and suggestions. Behavioral managers are persuasive, supportive,

empathetic, and they avoid conflicts.

Rowe and Mason (1987) have stated that a typical individual has one or two

dominant styles that are mentioned above. Furthermore, most people have

one or two backup (supportive) styles to be used. They stated that alignment

is the matching of an individual’s style to the specific demands of decision

making structures. Conceptual and Behavioral styles have less logical

approach compared to directive and analytical styles. Left hemisphere of our

brain is dominant if we use analytical and/or directive styles, which are task

oriented. On the other hand, if we use conceptual and/or behavioral style,

18

then right hemisphere of our brain becomes dominant, means that we are

people oriented.

Rowe and Boulgarides (1992) have stated that an individual’s decision style

may account for the behavior of that individual such as managing and

responding stress, problem solving capabilities, and the way he/she thinks.

Figure 3: Decision Style Model

(Martinsons & Davision, 2007)

Weighing alternatives includes numerically (quantitatively) weighing each

alternative’s importance related to given criteria (Dessler, 2004). Alternative

ranking or weighting can be objective i.e. based on known and certain facts,

subjective i.e. based on DM’s judgment as in fuzzy preference relations, or

evaluation, information obtained from literature survey, and experience

(Fülöp, 2005), or the integration of both objective and subjective

assessments to make the decision more credible and balanced (Wang &

Parkan, 2005). Decision matrix is used to illustrate and ease the process of

weighting or assessment of criteria based alternatives.

19

Pursuing right time for decision making is crucial. DM in a bad mood tends to

be more aggressive and intolerable, whereas, in better situations, DM tends

to be more relaxed, indulgent, and generous to a fault. Tolerance for faults is

a great advantage when the final decision couldn’t bring the desired results.

Improving creativity for better, original decisions is a must as Dessler (2004)

said. Rewarding and recognizing the creative ideas, encouraging

brainstorming without strictly criticizing the participants involved, being open

to different point of views and perspectives, opinions, management levels or

departments, all are acting as key factors for the creativity. Another point is to

provide suitable or comfortable places with interesting attributes for

participants and to allow written ideas for more introvert participants to

express their creativeness.

1.4.3 Psychological Traps That Cause Biases in Decision Making

Heuristics are the rules or approximations that are used in decision making

process through previous experiences gained to make a new choice, instead

of systematically following the decision making steps such as gathering as

much information as possible. Although heuristics may involve biases, they

may also be very useful in situations where imperfect information or under

uncertainty conditions exist (Gigerenzer & Gaissmaier, 2011). Anchoring is

another trap that offers itself by putting unnecessary high weights to the firstly

received information. Hammond, Keeney, and Raiffa (1998) define the

anchoring with an example of a marketer who tries to anticipate the future

sales of products in a dynamic marketing environment by only looking at the

previous years’ sales lists (first available information), which in turn produces

unanticipated sales results. They recommend DMs to check for more

available information from other people or sources, and to give enough

weight to other information achieved. Dessler (2004) describes psychological

set as the barrier approach to DM’s creativity to generate alternative

decisions. Difference in perception also considered as a trap in decision

making. As the all DMs’ points of views are not identical, interpreting

problems differently is inevitable. So, marketing manager must review the

20

organizational problems not only in point of marketing, but from financial, HR,

accounting, production, and etc. departments also.

1.5 Conclusion for Chapter 1

This chapter provides the basic principles and background information for

decision making process, which comprises both daily and administrative

decision making under certainty, risk, and uncertainty or with imperfect

information given. Decision making appears in every stage of our lives, so to

think out of box we need to break the barriers or psychological traps in front

of our creativity for more effective and efficient decision making process.

21

CHAPTER 2

HUMAN RESOURCE MANAGEMENT (HRM) AND

PERSONNEL SELECTION PROCESS

2.1 Importance of the HRM

Wright and McMahan (1992) have stated that Human Resource Management

system is the pattern of programmed-planned HR activities to be

accomplished in order to achieve the organizational goals and objectives. On

the other hand, all managers involve in basic recruiting, interviewing,

selecting, training, compensating personnel processes, and that is why all the

managers need these fundamental staffing skills. Also, HRM was used to

known as personnel management. (Dessler, 2004). Therefore, employee and

personnel are used interchangeably in this paper.

HRM consists of all management decisions and practices which have a direct

impact or influence on human recourses that are the main resources (capital)

for organizations in order to achieve organizational goals and objectives.

(Shahnawaz & Juyal, 2006). Strategic HRM is achieved by generating and

implementing the HR strategies which are integrated with the organizational

strategies that assist the company in accomplishing the general

organizational goals and objectives (Armstrong & Taylor, 2014). Becker and

Huselid (1998) put forward the significant positive impact of HR practices

such as recruitment and selection on personnel’s motivation, creativity, and

productivity. Huselid (1995) as cited in Wright, Gardner, and Moynihan

(2003), has emphasized the effect of these practices on lower turnover,

higher corporate performance, and more productivity.

22

According to Barney (1991) and, Lado and Wilson (1994) sustainable

competitive advantage can be achieved through HR practices by allowing

development of core competencies, generating organizational knowledge,

and building strong social relationships. The automated machines are

impracticable when there are no educated and trained human personnel or

workers to start those machines. Therefore, HRM is the essential, inimitable,

and bigger part of management and its practices should be prominent for the

organizations’ strategic success. (Dessler, 2004). Walton and Lawrence

(1985) state basic function areas of HRM as job analysis (design), hiring

required employee with appropriate personnel selection, training and

development practices for personnel improvement, rewarding and appraising

system (financial and motivational), influencing employee towards

participation, and employee compensation (wages, salaries, insurance,

incentives, etc.).

2.2 The Recruitment and Selection Processes

Huo, Huang, and Napier (2002) emphasized that the selection of the most

appropriate employees in order to cover the vacant positions or vacancies is

the fundamental purpose of all HR and line managers. As Dunnette and

Borman, (1979); also Mendenhall, (1987) had explained why this is important

for the organizations by emphasizing that the misfit between the employee

and the job could highly influence the performance of other HRM practices

and functions, and in turn all the organization.

2.2.1 Recruitment

Recruitment is the process of finding the most needed or desired resources

of an organization (Slusarczyk & Golnik, 2014). The managers who decide to

hire the new personnel should identify the basic job requirements through job

analysis as a first step. Then in accordance with the given information

company personnel or HRM develops the job description and job

specification. Job specification comprises the needed skills, experience and

traits needed by the personnel or employee to help to achieve the

organizational goals and objectives, whereas, job description includes the

23

information and summary about the job itself right alongside the required

duties of the job. (Dessler, 2004).

Attracting the possible applicants to the vacant position, or simply the

recruiting process can be conducted in two ways, which are within

company/internal selection, or through advertising. (Slusarczyk & Golnik,

2014).

Internal. Gusdorf (2008), states that internal selection or in other words,

promotion from within provides motivation, and enhances performance and

productivity of employees as a result of desire to achieve personal goals of

status and increased salary. The superiority of inside applicants is their

familiarity and likely commitment to company. Therefore, training of an

employee in order to learn company’s principles and policies is not required.

The drawback is the possible loss of more creative, productive, or expert

external personnel (Gusdorf, 2008). Also it is stated that promotion from

within is more applicable for top line managers.

Personnel referrals and job posting are used in internal selection.

Traditionally, publicizing the vacant faculty post was conducted through

posting the announcement on bulletin boards. However, as a result of

advanced technology announcements with job description or specification

are conducted through intranet and e-mail system. Current employee

references become more important when the process results with a win-win

situation or in other words, employer gains the new needed employee and

the existing employee receives the reward. (Gusdorf, 2008).

External. External recruitment can be accomplished through private

employee agencies where experts send the related skills possessed

applicants. Although its fee is high, process reduces the time needed for the

recruitment. Advertisement is another alternative to announce the open

position and appeal the applicants. Dessler (2004) emphasizes the

importance of appropriate medium of advertisement to attract the qualified

personnel. Announcements can be through local news papers and journals,

24

TVs, radios, internet in terms of online applications, social media

announcements, and etc. Today many big companies use internet or online

applications which decrease the cost, enable quick and lots of responses by

a simple process of posting the announcement on the homepage, and also

attract distant applicants, instead of traditional expensive and time

consuming advertising (Gusdorf, 2008).

2.2.2 Selection

Personnel selection is the decision made by the manager or in other words,

by the DM to choose the optimal or best fitted personnel among possible

alternative pool of applicants or candidates, using some selection tools and

techniques (Dessler, 2004; Gusdorf, 2008). Adequate selection of personnel

is crucial for the manager’s success, because the success of manager

depends on his/her personnel’s success in operating the given

responsibilities. As a consequence, good personnel selection enables the

accomplishment of organizational goals and objectives. (Slusarczy & Golnik,

2014).

According to Bratton and Gold (2003), validity and reliability play great roles

in the selection process. Reliability involves the control of whether the resent

evaluation (judgment) results of applicants are similar or same with the

results achieved in the past. Furthermore, test scores become reliable if the

results follow the similar pattern today. Validity involves the appropriateness

of the selection procedure that judges the candidates’ skills and abilities.

Although there are no uniform international technique for personnel selection,

most applied screening practices are tried to be discussed in this paper.

Interviews and psychometric tests are known as the most popular selection

or screening techniques among all of the selection devices (Harlan & Anne,

1980). Furthermore there are other techniques such as the resumes or CVs

of the applicants; application blanks to gather applicants’ background

information such as education, military status, language knowledge, past

experiences, etc.; work samples enable the DM or employer to demonstrate

25

the cognitive skills, or coordination and planning ability of an applicant in

implementing the knowledge to the practice; detectors of deception is used to

examine the tendency towards lying, theft, or honesty using tests as paper

and pencil; thought samples measure the motivation for achievement, power

and affiliation, but device is considered to be less reliable; and written

structured recommendations of applicants that shows the work history or

success of an applicants can be a good source for screening them. Prior to

conducting the selection process, managers should consider the reliability

and validity of the technique to be used. Selection technique should

appropriately measure the quality of the candidate or applicant associated

with the job requirements (Harlan & Anne, 1980). As it is mentioned by

Robertson and Smith (2001), overall researches show that applicants prefer

unstructured interviews and work samples rather than tests.

2.2.2.1 Interviews for Selection

Surveys put forward that the most preferred personnel selection technique is

interview. Interviews are used widely and in different concepts or formats.

Interviews can be grouped in three formats: structured interview, semi

structured interview, and unstructured interview.

• Least applied interview is structured interview. Relatively close-

ended questions prevent deviations from the interview schedule of

order of questions asked, and any possible probes, and also this

interview provides standardized questions rather than specific or

tailored (Harlan & Anne, 1980). Therefore, interviewer will not reach

an idea for the candidate’s certain behavior. Although these

weaknesses, many researchers have shown that structured interviews

are quicker to conduct, reliable and valid (Schwab & Heneman, 1969;

Wiesner & Cronshaw, 1988). According to Moscoso (2000) candidates

that possess comprehensive knowledge and job experience tend to

prefer structured questions as this type of interview may be best fit to

their positions. On the other hand, some of the researches put forward

the importance of judgmental assessment on executives’ selection

(Guion, 1998). Dipboye (1994) states that interviewers use more

26

formal job analysis to gather detailed information about required job

skills, abilities and knowledge by the structured interviews.

• Unstructured interviews consist of relatively open-ended questions.

Some questions could be added or eliminated as required, and this

explains why they sometimes called discovery interviews, and

interviewers hereby are highly motivated, as Harlan and Anne (1980)

state. This method enables candidate to deeply explain his/her own

feelings through choosing their own words to answer the questions.

Besides, it enables interviewer to understand the way of candidate

acts. This technique enables the interviewer to change or adapt the

different question for different candidates (Schmidt & Hunter, 1998) for

possible in-depth understanding of candidates’ capabilities. Dipboye

(1994) states that unstructured interviews are mostly preferred by NF

or intuitive-feeling style owned interviewers whose decisions are

mostly based on intuitive judgments and feelings (as it has been

mentioned in the first chapter). According to Dipboye (1994),

unstructured personnel selection interviews are less reliable and valid

than structured ones, because they include more subjective

information gathering and intuitive rankings. Halo effect can be given

as an example of cognitive bias. This situation can be described with

an example of beautiful or attractive candidate being perceived as

more intelligent and suitable for the job requirements although other

candidates may have similar or identical qualifications and skills.

Certainly, it may show differences within different occupations and

situations so, it is not correct to state a direct relation between

attractiveness bias and the personnel selection process (Chiu &

Babcock, 2002; Caki & Solmaz, 2013). However, candidates may

prefer unstructured interviews as they provide more sincere or

informal interview conditions, and enable to express themselves more

easily. Other possible reasons for this preference by interviewers may

be limited knowledge about structured interviews, and power desired

to judge the results or to prevent the monotony of structured questions

(Zee, Bakker, & Bakker, 2002).

27

• Most preferred type is semi-structured interview as Dipboye (1994);

Harlan & Anne (1980) state. It is comprised of the particular broad

area of questions such as related to past experiences, education life,

accomplishments or rewards, and etc. (Imada & Hakel, 1977). This

technique is based on gathering certain data about each candidate

that will provide fundamental, but usually not comparable information

as each interview is somehow specific by its length of time,

sequencing, phrasing, and different interviewee and interviewer. It is

considered as more time consuming format.

• There are also group and multiple interviews (Harlan & Anne, 1980):

Group interviews involve two or more (multiple) interviewers asking

questions jointly to a single candidate (sometimes called panel interview); an

interviewer with a group of candidates to be interviewed; or a combination of

them which includes multiple interviewers with multiple candidates.

Interviewers can compare the information gathered to understand the

applicants’ understanding of situation. Also it can give an idea about ability of

the applicant to work in a group. A group interview with multiple interviewers

ensures higher validity as it reduces possible biases through pool of

interviewers’ information (Dipboye et al., 2001).

Multiple interviews involve one candidate and two or more independent

interviewers in multiple independent interview sessions, asking different

questions. This reduces the possible biases for selection decisions and

makes it more valid technique through combining the assessment of one

interviewer by the assessments of another interviewer in order to fill the

knowledge gap (Harlan & Anne).

2.2.2.2 Personality/Psychometric Tests

Personality tests such as Big Five personality tests or Myers-Briggs

personality indicator based on Jung’s personality typology are conducted to

gain information about certain interpersonal and motivational strengths of the

candidates that in turn might affect job related performance and absenteeism

28

(Harlan & Anne, 1980). For instance, based on five main characteristics of

each candidate, they are scored and then according to the results achieved

through testing, managers, interviewers or DMs become able to decide

whether the candidate fits with the job requirements. Openness,

Conscientiousness, Extraversion, Agreeableness and Neuroticism are the big

five personality factors. For example, for veterinary, friendly and careful

nature is required, which in turn depends on the ‘openness’ and

‘agreeableness’ characteristics. According to 12 years study on the

personality tests’ validity of usage in personnel selection process, it is shown

that personality tests might be merely valid when it is applied to specific

department with a specific purpose and situation (Guion & Gottier, 1965). For

instance, some tests may be valid for a particular department whereas,

similar tests may be invalid within a different department. Therefore, DM

should do the necessary research about how to measure and test personnel

in a particular situation and with a particular purpose before conducting the

test. On the other hand, according to Barrick and Mount (1991) personality

test of Big Five personality dimensions have a particular effect on job

performance of personnel from different occupations. Their study concludes

that Conscientiousness, Extraversion, and Openness to Experience have

positive significant impact on job performance and they are valid predictors.

Other dimensions (Agreeableness and Emotional stability) are also valid

predictors but with less positive correlation. Many studies for example,

Christiansen et al. (1994); Ones and Viswesvaran (1998) have underscored

the validity and reliability of personality tests in personnel selection by testing

the fake responses given by the candidates that highly reduce the validity.

Faking practices of candidates exist as a result of the desire to create a good

image through responding by desired right or positive answer, unless the

candidates try to be honest (Morgeson et al., 2007). Consequently, faking

present candidates will alter the test’s results that include biases in ranking

order of candidates. Morgeson et al., (2007) put forward that although faking

indicators are not effective in increasing validity, it may be useful to use

questions that provide multiple statements or scales with equal perceived

desirability responses and in turn candidates without careful and linked

responses in personality tests might be removed.

29

2.2.2.3 Clinical and Statistical Approaches

Clinical selection approach is based on the interviewer’s or DM’s judgment

on an applicant’s test scores and information gathered from the

(unstructured) interview (Born and Scholarios, 2005). DM’s perception plays

a big role in selecting the appropriate candidate to fit the vacancy.

Experience and intuition of DM enable him/her to turn the data gathered into

a final decision (Färber, Weitzel, & Keim, 2003). Färber, Weitzel, and Keim,

(2003) have stated that although the subjectivity and unreliability exist in the

decision, clinical or judgmental selection approach might be very useful than

statistical or mechanical solutions in some situations. They explain

mechanical approach as objective, isolated from human interference, and

transparent data that should be quantifiable. Statistical method for the

personnel selection process might be handled using weights to assign the

most necessary criteria through detailed job analysis and job description

(Born and Scholarios, 2005). For instance, applicants might be needed to get

at least the minimum required score for each necessary and most required

criterion given during interview, tests or work samples (the multiple cut-off

model). Finally, DMs compare applicants according to achieved scores and

make selection decision accordingly. Researches has shown that mechanical

method is more valid and reliable than clinical/ judgmental method, but the

combination of two separate methods generates even more accurate results

(Ganzach, Kluger, & Klayman, 2000; Färber, Weitzel, & Keim, 2003; Carless,

2009). An example for this combination might be integration of DM’s overall

aggregated judgment about an applicant with his/her scores received from

tests and interviews.

2.3 Conclusion for Chapter 2

We can conclude with an overall interpretation about recruitment and

selection practices as the fundamental practices of HRM that may provide a

competitive advantage for the company in the long run. Furthermore, in order

to achieve this competitive advantage DMs should avoid biases or errors in

personnel selection processes. Being able to select the most suitable

personnel that matches the job requirements involves decision making based

on more rational and statistical choices through weighting or ranking

30

personnel, instead of decision that is merely based on more intuitive or

judgmental interpretations (clinical approach).

Many researches put forward the related studies about the validity of

interviews. As a result, it is indicated that for many situations and occupations

more structured interviews provide more validity, or in other words, they

measure what is required - the most suitable personnel.

Personality tests which are mostly used in personnel selection might be

improved to increase their validity by decreasing the level of fake responses

of candidates. In fact, it is difficult to let the level down but, the studies on

providing scales of answers in tests rather than merely true or false

statements might be improved more to be able to achieve more reliability

(achieving similar or consistent results over time) and validity.

To sum up, human resources are the fundamental capital of the

organizations even the organizations that use mechanical machines which

are working according to written rules and information gathered from human

experts. Therefore, external or internal recruiting, selecting practices, training

and apprising personnel are all crucial and must be carefully conducted in

order achieve the organizational objectives, strategies and goals.

31

CHAPTER 3

PERSONNEL SELECTION DECISION BASED ON SELF-

CONFIDENCE

3.1 Preference Relations-Representation of the Preferences

As it is mentioned in the previous chapters, decision making process that is

selecting the feasible optimal alternative or option among possible

alternatives set is transpired almost every phase of our lives. Herewith

studying on decision making processes becomes crucial in many fields of

study such as Social Psychology, Operations Management, AI (Artificial

Intelligence), etc. as, Chiclana, Herrera, and Herrera-Viedma (1998) have

stated. In the fields of Management Science, especially HR, industrial

engineering, and other business management practices, decisions made

associated with the appropriate personnel selection have huge impact on

organizations’ strategic success. An example of personnel selection problem

based on multi-criteria decision making that comprises 5 criteria and 6

alternative candidates is given in one of the well-known books (Aliev & Aliev,

2001). Here, authors have used compositional rule of inference in order to

choose the optimal alternative candidate in accordance with the given

required criteria. Bogdanovic and Miletic (2014) put forward the efficiency

and the success of multi-criteria decision making approach in personnel

selection and evaluation process by improving the approach through

integration of Analytic Hierarchy Process (AHP) which is developed by Saaty

(1980), that eases the process of comparing the weighted criteria based on

expert’s or DM’s preferences, and determining the overall priority of

alternatives based on each criterion; and PROMETHEE (Preference Ranking

Organization Method for Enrichment Evaluation) that is developed by Brans

32

and Vincke (1985), which method enables the DM to decide based on the

final ranking of alternatives with preference function. Dağdeviren (2008)

suggests fuzzy AHP usage whenever criteria preferences obtained from the

DM include impreciseness or uncertain information that might be in

qualitative structure. For example, Fuzzy AHP is efficient in representing

uncertain or imperfect information based preferences of the DM through

enabling the use of intermediate boundaries for value expressions, instead of

a single value to generate evaluation matrix (Chan et al., 2008). Other fuzzy

approach based multi-criteria studies also have effectively disclosed the

importance of decision making in different areas (De Brucker, Macharis, &

Verbeke, 2011; Pohekar & Ramachandran, 2004; Kahraman, Cebeci, &

Ulukan, 2003; Jiang & Eastman, 2000).

Some other important methods for personnel selection have been performed

and achieved effective solutions. One of them is TOPSIS decision tool that

provides both positive-ideal and negative-ideal solutions and gives the

ranking of alternatives to enable the DM to select the optimal personnel that

matches with the job requirements (Sang, Liu, & Qin, 2015; Şenel, Şenel, &

Aydemir, 2018). Hudson, Reinerman-Jones, and Teo, (2017) review different

decision making approaches for effective personnel selection process that

indeed plays a key role for organizational success.

It is seemed from the previous researches and studies that pair comparison

of one alternative over another alternative, or in general words, preference

relations that represent the DM’s expression of opinions over possible

alternative sets, are significantly related to efficiency and efficacy of the

overall decision making process. For example, in personnel selection

preference relations on a pair of alternative candidates can be represented

by the degree of preference or intensity for alternative candidate i over

alternative candidate j, i.e. ( )jiij xx , = with XX matrix, where X

stands for the set of possible alternative candidates for the vacant faculty

post. However, different DMs may follow different approaches to provide their

preferences or comparisons over the fuzzy set of alternatives X, because of

33

different or special personality, motivation factors, attitudes and ideas

possessed by each DM (Chiclana, Herrera, & Herrera-Viedma, 2001).

Therefore, Chiclana, Herrera, & Herrera-Viedma (2001) state the mainly used

preference relations or evaluations by DM that may be used in personnel

selection as well. Widely used preference relations include fuzzy preference

relations (Chiclana, Herrera, & Herrera-Viedma, 1998; Fodor & Roubens,

1994) multiplicative preference relations (show ratio of preference for ix over,

jx ), utility functions (refer to the DM’ utility evaluations) and linguistic

preference relations (Herrera-Viedma et al., 2004; Xu, 2004, Herrera-Viedma

et al., 2005, etc.).

3.2 Fuzzy Preference with Self-confidence

Novelty confronts us when we take into account the self-confidence level of

the DM, which is the consideration that was unfortunately not studied, or

neglected in the above considered and other related researches.

As it is stated in Zarnoth and Sniezek (1997), confidence level of a DM is

related to how strongly he/she believes the specific statement given is the

optimal response. Previous study about reciprocal intuitionistic fuzzy

preference relations was integrated with confidence/consistency level on the

expert DMs’ opinions (Ureña et al., 2015). Novel approach was put forward in

Liu et al. (2016) that is, fuzzy preference relations with self confidence level.

In our study we implement the fuzzy preference relations with self confidence

on personnel selection problem in order to choose the optimal candidate that

matches with the vacant job requirements.

3.3 Preliminaries

In this part we cover the basic information about the fuzzy preferences,

linguistic variables for self-confidence (Aliev & Aliev, 2001), and fuzzy

preference with self-confidence (Liu et al., 2016; Liu et al., 2017).

34

Fuzzy preference relation (also sometimes called additive preference

relations) P on a finite set of possible alternatives nxxxX ,...,, 21= , is a

relation on XX (matrix) with membership function of 1,0: → XXp , and

( )ijjip pxx =, , where Pij indicates the degree/intensity of preference of

alternative ix over jx of DM (Liu et al., 2016). Fuzzy preference relation

might be indicated by ( )nnijpP

= , where nn is a matrix.

5.0=ijp , denotes indifference between ix and jx ,

5.0ijp , denotes a definite preference for ix over jx ,

1=ijp , denotes the maximum degree of preference for ix over jx ,

With the assumption of; 1=+ jiij pp , and 5.0=iip (for the verified asymmetry

of given preferences).

Linguistic variables for self-confidence indicate the linguistic terms used

to describe the self confidence level of the DM. As it has been covered in

chapter 1, linguistic variable has values that are expressed in natural

language (NL) or in other words, values are in the form of words or

sentences instead of numerical quantities (Zadeh, 1975). The purpose is to

be able to achieve a satisfying solution or decision for the real world problem

which involves grand human thought processes or decision making

complexity. Therefore, Zadeh (1975) developed the principle in order to deal

with the vagueness and uncertainty present in humanistic systems, which

systems are affected by human judgment, perception, and feelings

(educational systems, political systems, economical systems, etc.). Therefore

we might also conclude that based on Rowe and Mason’s (1987) decision

styles, Analytical style and Conceptual style user DMs that have high

tolerance for ambiguity and cognitive complexity, and able to work in

uncertain environment may use Zadeh’s (1975) principle of linguistic variable

and fuzzy logic to decide in a real world framework. Zadeh (1975) states that

by using linguistic variables, tolerance for impreciseness can be exploited to

achieve a robust solution and to decrease the solution cost.

35

The potential advantage of using linguistic variables is its characterizations

that do not restrict the DM to represent the information in the form of specific

or crisp values. Our previous example covers Age linguistic variable with its

possible linguistic values (and hedges) of ‘young, not young, middle aged,

not very old, old, and extremely old’, etc. within the linguistic variable Age’s

term-set. Naturally, age is expressed in numbers such as 1, 2, 3, 4, 5, 6,

7,…, 100, etc. so these numbers (values) underlie the base variable for Age.

Using the base variable’s values linguistic value ‘old’ within the term-set of

Age may be represented as a label for fuzzy restriction (what we mean by

old) that is characterized by a compatibility function. Compatibility function for

the fuzzy restriction indicates the association between each value in the base

variable, and a number in the 1,0 interval. For instance, compatibilities of

the quantified ages 21, 28, and 35 for the fuzzy restriction labeled ‘young’

may be 1, 0.7, and 0.2, respectively (Zadeh, 1975). Illustration for this

example is given below in figure 4. Furthermore, hierarchical structure for the

relation between Age variable, its fuzzy restrictions, and base variable values

are given in figure 2.

Figure 4: Compatibility Function Example for ‘young’ Labeled Fuzzy Restriction

(Zadeh, 1975)

Linguistic terms might be used for other variables such as Height, Weight,

Truth, etc., also. For Truth, linguistic terms or values might be true,

moderately true, extremely true, untrue, and so forth. An example of weather

36

forecasting may be given as “most probably it will rain tomorrow” instead of

“80 percent it will rain tomorrow”, or “cold day” are all fuzzy events because

human understanding of weather dynamics is not enough for precise

indication of rain probability (Zadeh, 1975).

On the basis of above mentioned reasons, in this paper we use linguistic

terms to describe self-confidence levels of DMs, for example;

S={low, medium,…, very high} (1)

Fuzzy preference with self-confidence is the new subject that we aim to

use for more reliable and correct decision makings. According to Liu et al.

(2016) DMs do not always denote self-confidence on the preceding

preference due to limited or scarce time, excellence or knowledge. Moreover,

previous studies take into account only two types of self-confidence levels

that are either DM is with self-confidence for the given preference

information, or DM is without self-confidence if the preference information

based on pair wise comparison of alternatives is not given. On the other

hand, multiple self-confidence levels can be denoted by DMs, if the proper

and suitable mathematical tools and technique is provided. Within this type

of preference relation, each alternative element comprises two parts. First

part involves DM’s actual fuzzy preference value in terms of a pair of

alternative candidates. The second part indicates the DM’s self-confidence

level that is described in ordinal linguistic terms scale associated with the

preceding fuzzy preference value provided. This index is represented as

follows;

( )ijij SPP ,= (2)

Where, ijP denotes the degree of preference of alternative ix over alternative

ix , and ijS denotes the self-confidence level on the

ijP .

Liu et al. (2018) propose the self-confident multiplicative preference relations

usage to investigate the consensus based group decision making and

according to this process they have found out some robust results such as:

37

1) information loss from preferences may be reduced by providing self

confidence levels; 2) using multiplicative preference relations with self-

confidence level adjustments can improve the success ratio and the speed of

the decision making process.

Zadeh (2011) as cited in Liu et al. (2017) proposes the use of Z-number

principle which is somewhat associated with using linguistic self-confidence

levels on preference evaluations. Zadeh (2011) states that each Z-number

consists of two parts A and B, i.e. Z= (A, B). A stands for the (fuzzy)

constraint or restriction, while, B the second component indicates the

measure of certainty or reliability on the first part A. Both A and B are given in

natural language, NL (e.g. 90 days, very sure).

3.4 Methodology for the Solution

As we have mentioned earlier, selecting the optimal or best candidate among

the possible group of applicants that is best suited for the particular vacant

position’s requirements and the organization may in turn supply the

organization with the competitive advantage. Thereby, here we understand

the importance of the personnel selection technique and problem.

The problem is to choose the optimal candidate for a given vacant faculty

post. Here we are considering the job related multiple criteria given according

to job analysis and job description (see Appendix A) to be able to compare

the possible alternative candidates, and make a selection. The criteria used

for decision making are given as follows (here we assume all criteria as

equally important for the faculty, i.e. all the criteria given below have equal

weights, w):

• Publication Results (C1)

• Industrial Experience (C2)

• Teaching Quality (C3)

• Grant Taking Ability (C4)

• Intelligence Level (C5)

38

(Vacant faculty position might be assumed for instructors or academician

candidates in a particular university.)

There are 5 possible alternative candidates. This can be shown as a finite

set:

54321 ,,,, aaaaaA =

The aim is to derive the priority vector of fuzzy preference relation with self

confidence w= {w1, w2, w3, w4, w5}, that minimizes the total information

deviation between the DM’s (fuzzy) preference relations and vector w. At the

end, ranking of candidates as {w1, w2,…, w5} gives the DM ability to find and

choose the best alternative candidate.

We use pair wise comparisons over all possible alternatives that are

associated with our personnel selection problem (multi-criteria decision

making (MCDM) problem) which is based on various criteria and candidates

(alternatives). To sum up, our objective or aim is to select the most

appropriate personnel for the vacant position, with the given criteria

(publication results, industrial experience, technical skills, and so forth), and

available alternatives (personnel candidates A1, A2, and so forth). There are

some steps as follows:

1. Relative importance of each decision criterion is determined by the

DM. In this example study, each criterion possesses equal weights.

2. Pair wise comparisons or evaluations for alternatives (in our case

candidates) in terms of each criterion are conducted according to

fuzzy preferences of an expert DM.

3. Aggregated preference matrix over the all alternatives is calculated by

synthesizing all of the pair wise fuzzy preference matrix results into

one aggregated fuzzy preference matrix.

4. Furthermore, related consistency of the indicated preferences over the

alternatives is calculated by Consistency Index (CI) based on

eigenvector method developed by Saaty (1980).

39

Figure 5: Research Model

Each criterion is regarded as equally important. We perform pair wise

comparisons for each pair of alternatives Ai, 5,1=i with respect to each

criterion Ci, 5,1=i considered. In this regard, we have to repeat this

evaluation five more times for each criterion given. An example of fuzzy

preference relation matrix for criterion 1 (C1) is presented below (table 1).

C1 A1 A2 A3 A4 A5

A1 0.5 p12 p13 p14 p15

A2 p21 0.5 p23 p24 p25

A3 p31 p32 0.5 p34 p35

A4 p41 p42 p43 0.5 p45

A5 p51 p52 p53 p54 0.5

Table 1: Fuzzy Preference Relation with Respect to C1

Table shows the pij which indicates the degree or intensity of preference of

alternative candidate Ai over alternative candidate Aj. Also it is assumed that,

1=+ jiij pp , and 5.0=iip . For example, pair wise comparison of A3 over A5

gives p35=1-p53, and for alternative A3 p33=0.5.

In the next step after each (five) fuzzy preference evaluations of alternatives

based on each criterion is performed, we need to generate an aggregated

preference matrix by aggregating all five fuzzy preference matrices obtained.

Aggregated matrix is shown below in table 2.

•Importance of each criterion is identified

•Pair wise comparisons or evaluations for alternatives (in our case candidates) in terms of each criterion are conducted

•Aggregated preference matrix over the all alternatives is calculated

•Consistency Index (CI) and Consistency Ratio (CR) based on eigenvector method is calculated

40

Table 2: Aggregated Preference Matrix

In this new approach of fuzzy preference relations with self-confidence DM

firstly provides the preference values considering the pair of alternatives as it

is shown in table 2. However, in the real world situation or practice this matrix

is frequently inconsistent because of some account error given by the expert

DM on preference evaluations. In the case of full consistency λmax=n and the

maximum eigenvalue is denoted as λmax. Deviation from consistent

approximation of evaluations by an expert DM is expressed by λmax≥n. Thus,

to ensure and measure consistency of provided evaluations we compute

Consistency Index (CI) developed by Saaty (1980). Equation is given below.

max

1

nCI

n

−=

−

Here, λmax indicates the maximum eigenvalue, where n is the number of pair

wise comparison matrices ℝ (in our case 5). To compare this value Saaty

(1980) suggests using Random Index indicated by RI, which is simply the

estimated average CI acquired from a large number of randomly generated

reciprocal (fuzzy preference) matrices of the same order (Table 3).

n 1 2 3 4 5 6 7 8 9 10 11 12

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48

Table 3: Random Index (RI)

(Saaty, 1980)

The matrix is accepted as consistent as long as the ratio CI/RI is less than or

equal to 0.1, otherwise preference evaluations are accepted as inconsistent.

A1 A2 A3 A4 A5

A1 (0.5) (0.9) (0.9) (0.2) (0.9)

A2 (0.1) (0.5) (0.9) (0.1) (0.3)

A3 (0.1) (0.1) (0.5) (0.3) (0.2)

A4 (0.8) (0.9) (0.7) (0.5) (0.8)

A5 (0.1) (0.7) (0.8) (0.2) (0.5)

(3)

41

Here, threshold 0.1 or CI=0.1 means only the 10% judgment by the expert is

inconsistent as a result of random judgment. Ratio values more than 0.1 are

not considered as appropriate for a consistent decision making.

In our case consistency index based on matrix presented in Table 2 is

calculated as follows (Table 4 and Table 5).

A1 (0.5+0.9+0.9+0.2+0.9)/5= 0.68

A2 (0.1+0.5+0.9+0.1+0.3)/5= 0.38

A3 (0.1+0.1+0.5+0.3+0.2)/5= 0.24

A4 (0.8+0.9+0.7+0.5+0.8)/5=0.74

A5 (0.1+0.7+0.8+0.2+0.5)/5=0.46

Table 4: Eigenvector Calculation

Eigenvector 0,68 0,38 0,24 0,74 0,46

Total sum 1,60 3,10 3,80 1,30 2,70

Max ((0.68*1.6)+(0.38*3.1)+(0.24*3.8)+(0.74*1.3)+(0.46*2.7))=5.38

Table 5: λmax (Maximum Eigenvalue) Calculation

Calculation of Consistency Index (CI) and Consistency Ratio (CR) by using

the formula is given below.

095.015

538.5=

−

−=CI

1.008.012.1

095.0===

RI

CICR

Hence, we can conclude that, as 0.08<0.1, preference relation shown in

Table 2 is consistent enough to make a proper decision.

After consistent fuzzy preference value elicitation, expert DM provides the

self-confidence levels associated to the preceding part that consists of

preference values. Self-confidence is described by linguistic terms.

42

Linguistic term set gSL llllS ,...,,, 210= (where l0 may denote very low; l1 may

denote low; or l5 may stand for extremely high) is used by the DM to

characterize their self-confidence levels in the linguistic way. Assumptions for

this method comprise 1=+ jiij pp ; 5.0=iip ; jiij ss = ; and

gii ls = .

In our case linguistic term set is described as SSL= {l0=very low, l1=low,

l2=poor, l7=high, l8=very high}.

43

CHAPTER 4

RESULTS

Fuzzy preference relations information that is gathered from the expert or

experienced DMs such as managers involves considerations, comparisons,

and rankings based on each pair of possible alternative candidates. These

considerations and rankings are performed based on previously identified

criteria evaluations. Interviews and tests may be performed in order to

ascertain and confirm whether the ability of the candidate meets the

requirements related to job criteria. Here, we use a linear programming (LP)

model to find the priority vector of fuzzy preference relation with self-

confidence. Application of the LP model enables the simpler computation in a

shorter time period, and can increase the precision of the solution or decision

obtained. Linear programming is a quantitative technique used for the

solutions of resource allocation problems. For example, for designing

shipping schedule, selecting the best or optimal production mix to maximize

the firm’s profit or minimize the cost through best use of available machine

and labor hours in a particular factory, or assigning personnel to various jobs

as it is in our case (Dessler, 2004).

Fuzzy self-confidence related preference relation matrix is represented in

table 6.

A1 A2 A3 A4 A5

A1 (0.5, VH) (0.9, VH) (0.9, H) (0.2, VH) (0.9, H)

A2 (0.1, VH) (0.5, VH) (0.9, VH) (0.1, VH) (0.3, VH)

A3 (0.1, H) (0.1, VH) (0.5, VH) (0.3, VH) (0.2, H)

A4 (0.8, VH) (0.9, VH) (0.7, VH) (0.5, VH) (0.8, VH)

A5 (0.1, H) (0.7, VH) (0.8, H) (0.2, VH) (0.5, VH)

Table 6: Fuzzy Preference Relation with Self-Confidence

44

We use VH and H as our linguistic terms to enable the DMs to characterize

or express their self confidence levels on the given preference values, that

are described by triangular fuzzy numbers as: A= (a, b, c), i.e. a- lowest

value, b- middle/mean value, and c- highest value, respectively. Using

triangular fuzzy numbers as shown in figure 5 instead of crisp values (0 or 1)

provides tolerance for imprecise self-confidence levels of DMs (Zadeh,

1975).

SSL= {l0=very low, l1=low, l2=poor, l7=high, l8=very high}, where (mostly used);

H= High= {0.7, 0.8, 0.9}

VH= Very High= {1, 1, 0.9}

Figure 6: Triangular Fuzzy Numbers

In order to find the priority vectorw of fuzzy preference relation with self

confidence levels we formulate the following linear programming model (4)

(Liu et al., 2016). This linear programming model provides the priority vector

that minimizes the sum of information deviation indicated as ijz , between the

DM’s first preference value on pair of alternatives and the priority vectorw .

Objective function:

z=z12+z13+z14+z15+z23+z24+z25+z34+z35+z45 →min

subject to

𝜇

(4)

High Very high

45

0.5w1-0.5w2-y12= (0.9-0.5) = 0.4

0.5w1-0.5w3-y13= (0.9-0.5) = 0.4

0.5w1-0.5w4-y14= (0.2-0.5) = -0.3

.

.

.

0.5w2-0.5w5-y25= (0.3-0.5) = -0.2

0.5w3-0.5w4-y34= (0.3-0.5) = -0.2

0.5w3-0.5w5-y35= (0.2-0.5) = -0.3

0.5w4-0.5w5-y45= (0.8-0.5) = 0.3

Z12-4*y12≥ 0

Z12+4*y12≥ 0

Z13-3*y13≥ 0

Z13+3*y13≥ 0

.

.

.

Z45-4*y45≥ 0

Z45+4*y45≥ 0

1 2 3 4 5 1w w w w w+ + + + =

  0, 1,5iw i =

  0, , 1,5ijz i j =

46

Solution of the linear programming model (4) gives us priority vector of the

alternative candidates as:

w = {0.4, 0, 0, 0.6, 0}

The optimal alternative is the candidate with the highest overall assessment

value w. As a result we can conclude that the best candidate is A4= 0.6 (forth

alternative candidate), with minimized total information deviation of z=6.9.

Therefore, DM or manager is able to make decision on the optimal or most

appropriate candidate, i.e. alternative 4, who is the most suitable and feasible

personnel to hire.

To prove the validity and the feasibility of this new approach with self-

confidence another example with different preferences can be given.

Alternative example is given below.

A1 A2 A3 A4 A5

A1 (0.5, VH) (0.4, VH) (0.6, H) (0.8, VH) (0.7, H)

A2 (0.6, VH) (0.5, VH) (0.7, VH) (0.9, VH) (0.8, VH)

A3 (0.4, H) (0.3, VH) (0.5, VH) (0.7, VH) (0.6, H)

A4 (0.2, VH) (0.1, VH) (0.3, VH) (0.5, VH) (0.4, VH)

A5 (0.3, H) (0.2, VH) (0.4, H) (0.6, VH) (0.5, VH)

Table 7: Alternative Example

(Eyupoglu & Imanova, 2018)

Again LP model is used to find the best alternative with the minimized total

information deviation:

Objective function:

z=z12+z13+z14+z15+z23+z24+z25+z34+z35+z45 →min

subject to

1 2 120.5 0.5 (0.4 0.5) 0.1w w y− − = − = −

1 3 130.5 0.5 (0.6 0.5) 0.1w w y− − = − =

1 4 140.5 0.5 (0.8 0.5) 0.3w w y− − = − =

1 5 150.5 0.5 (0.7 0.5) 0.2w w y− − = − =

(5)

47

2 3 230.5 0.5 (0.7 0.5) 0.2w w y− − = − =

2 4 240.5 0.5 (0.9 0.5) 0.4w w y− − = − =

2 5 250.5 0.5 (0.8 0.5) 0.3w w y− − = − =

3 4 340.5 0.5 (0.7 0.5) 0.2w w y− − = − =

3 5 350.5 0.5 (0.6 0.5) 0.1w w y− − = − =

4 5 450.5 0.5 (0.4 0.5) 0.1w w y− − = − = −

12 125* 0Z Y−

12 125* 0Z Y+

13 133* 0Z Y−

13 133* 0Z Y+

14 145* 0Z Y−

14 145* 0Z Y+

15 153* 0Z Y−

15 153* 0Z Y+

23 235* 0Z Y−

23 235* 0Z Y+

24 245* 0Z Y−

24 245* 0Z Y+

25 255* 0Z Y−

25 255* 0Z Y+

34 345* 0Z Y−

34 345* 0Z Y+

35 353* 0Z Y−

35 353* 0Z Y+

45 455* 0Z Y−

45 455* 0Z Y+

1 2 3 4 5 1w w w w w+ + + + =

  0, 1,5iw i =

  0, , 1,5ijz i j =

Here, solution (5) gives a different priority vector w={0.33, 0.53, 0.13, 0, 0}.

Therefore, with different preference evaluations and self-confidence levels

the best alternative candidate becomes A2, with z=2.9.

48

Herby, we can state that fuzzy preference values provided with self-

confidence levels by DMs have a significant impact on the decision making

results, and use of another example provides generalization.

49

DISCUSSION AND CONCLUSION

To sum up, in the last chapter we investigate a new kind of preference

relations with self-confidence levels of DMs. Priority vector for candidate

selection is obtained by formulating a linear programming model. Generally,

decision makers have multiple self-confidence levels on their preferences

provided, instead of either full self-confidence or without any self-confidence

statements. So we can conclude that using fuzzy logic for preference

relations on each possible pair of alternative candidates, and for expressing

self-confidence levels on the given preference values exploits the tolerance

for imprecision; improves subjective expressions or judgment; and enables

DMs to work under uncertainty and with subjectivity in order to solve a real

world problem such as personnel selection (Zadeh, 1975). Also, by this

methodology of weighting each alternative in respect of each criterion and

providing preferences according to expert’s judgment, we somewhat provide

an integrated method that includes both clinical and mechanical approaches.

Furthermore, a numerical example for personnel selection decision is

provided that gives us the optimal candidate for the vacant faculty position,

an alternative example with different values is provided besides to prove the

feasibility and validity of the approach.

In this paper we investigate the decision making process that appears almost

in every phase of our lives. Therefore, decision making has become a crucial

part of management as well. Human Resources Management, which involves

the management of the most important capital of the organization- human

being or personnel, is a managerial function that all managers somehow

possess. Most of the managers take part in recruiting, interviewing, selecting

personnel, and training processes in the case of necessity. One of the most

sophisticated, and uncertainty or imprecision included staffing functions is the

personnel selection process that involves interviews, various kinds of tests,

and accordingly provided alternative candidate preferences, etc. in order to

choose the best or the most optimal candidate for the vacant position, and in

turn to accomplish the organizational goals and objectives, or a competitive

50

advantage in the market. Here we have formulated a new decision model on

personnel selection process taking into consideration the self-confidence

levels on fuzzy preference relations of decision makers (DMs) denoted in the

linguistic terms. Using fuzzy approach allows DMs to indicate incomplete

information knowledge on expressing their preferences. The priority vector of

fuzzy preference relations with self-confidence, on pairs of alternative

candidates and identified criteria, is obtained through the use of linear

programming (LP) tool such as Linear Program Solver Optimization Package

which minimizes the total information deviation ijz between the DM’s (fuzzy)

preference and the priority vector w . The priority vector obtained enables the

DMs to find the best alternative. Finally, the numerical examples given on

decision making to choose the optimal alternative candidate for a vacant

faculty post has proved the validity of the considered approach. Considered

approach can be applied to group decision making problems for the future

researches as well.

51

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60

APPENDIX A

Example of Job Description Template

Job Title: Job Category:

Department/Group: Job Code/ Req#:

Location: Travel Required:

Level/Salary Range: Position Type:

[i.e.: full-time, part-time, job share, contract, intern]

HR Contact: Date posted:

Will Train Applicant(s): Posting Expires:

External posting URL:

Internal posting URL:

Applications Accepted By:

Fax or E-mail:

(425) 555-0123 or someone@example.com

Subject Line:

Attention: [Recruiting or HR Department RE: Job Code/Req# and Title]

Mail:

[Recruiting Contact or Hiring Manager]

[Department, Company Name]

[P.O. Box]

[Street or Mailing Address with ZIP Code]

Job Description

Role and Responsibilities

[Type a description of the essential roles, responsibilities and activities a candidate can expect to assume in this position, using the Details style. For bullets, use the Bulleted List style:

Bulleted list item

Bulleted list item

For a numbered list, use the Numbered List style:

Numbered list item

Numbered List item]

Qualifications and Education Requirements (or related Criteria)

[Type a description of the work experience and educational background that a candidate should have when applying for position. Use the Details, Bulleted List, and/or Numbered List styles as needed.]

Preferred Skills

[Type a description of any additional skills or experience that would be considered favorable for a candidate who is applying for this position. Use the Details, Bulleted List, and/or Numbered List styles as needed.]

Reviewed By: Date:

Approved By: Date:

Last Updated By: Date/Time:

Source: https://templates.office.com/en-us/Job-description-form-TM10357174